实时网络流量分类研究综述  

Survey on Real-time Traffic Classification

作　　者：柏骏[1] 夏靖波[1] 吴吉祥[1] 任高明[1] 赵小欢[1] 

机构地区：[1]空军工程大学信息与导航学院,西安710077

出　　处：《计算机科学》2013年第9期8-15,共8页Computer Science

基　　金：陕西省科技计划自然基金重点项目(2012JZ8005);全军军事学研究生课题(2010XXXX-488)资助

摘　　要：实时流量分类技术能够按照应用类型对在线网络流量分类,它对网络管理、流量控制以及网络相关研究具有重要意义。首先从不同层次上简单分析了实时流量分类技术的研究现状;给出了流量分类的实时性概念及其指标;然后从流量统计特征和机器学习算法两个方面综述了实时流量分类的主要技术及研究进展,并进行了实时性分析;最后根据未来网络发展对实时流量分类技术提出的新要求,展望了该领域未来的研究发展方向。Real-time traffic classification, identifying and classing online traffics according to their applications, is of fun-damental importance to network managements, operations and correlative studies. The paper first analyzed the research statusquo of real-time traffic elassificaiton from different levels. The definition and evaluation metric of real-time classif-caiton were given to address the situation that there is no measurable and accepted metrics. Next, the main techniques and research progresses of real-time traffic elassifi-cation were summarized while the real-time property of eath tech-niques were analyzed from flow features and machine learning shcemes. Finally, we pointed out possible future research directions in real-time traffic classification according to the demand of future network.

关 键 词：流量分类 实时性 流特征 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]